提出Bi-Band ECoGNet模型，提升脑电分类精度与训练速度。
Bi-Band ECoGNet for ECoG Decoding on Classification Task
- 设计双频通道变换模块，替代传统耗时方法，提升计算效率。
- 融合高低频信息并利用二维电极结构，分类准确率提升1.24%。
- 模型更小更快，适合实时脑机接口应用，尤其适用于视觉任务。
在脑机接口（BCI）应用中，准确解码脑电信号至关重要。针对高密度电极阵列采集的皮层脑电（ECoG）多类分类任务，如何提升分类精度是当前研究热点。由于ECoG数据在时间域具高相似性与冗余性，空间域具独特模式，特征提取面临挑战。已有研究表明，视觉相关ECoG信号可通过频率与空间域传递视觉信息。基于此，本文提出一种基于深度学习的Bi-Band ECoGNet模型，核心贡献包括：1）设计新型双频通道变换（Bi-BCWT）模块，替代耗时的MST方法，显著提升计算与存储效率，训练速度加快6倍；2）该模块能有效融合低频与高频信息，更适配多类分类任务；3）针对二维电极阵列结构，设计2D时空特征编码器，更好捕捉二维空间特征。实验表明，该结构可有效提升分类准确率，相比先前方法，模型精度提高1.24%，且模型体积更小，更适合真实场景下的脑机接口部署。
原文摘要 · Abstract (English)
In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array and a high sampling frequency, which makes ECoG data have a certain high similarity and data redundancy in the temporal domain, and also unique spatial pattern in spatial domain. How to effectively extract features is both exciting and challenging. Previous work found that visual-related ECoG can carry visual information via frequency and spatial domain. Based on this finding, we focused on using deep learning to design frequency and spatial feature extraction modules, and proposed a Bi-Band ECoGNet model based on deep learning. The main contributions of this paper are: 1) The Bi-BCWT (Bi-Band Channel-Wise Transform) neural network module is designed to replace the time-consume method MST, this module greatly improves the model calculation and data storage efficiency, and effectively increases the training speed; 2) The Bi-BCWT module can effectively take into account the information both in low-frequency and high-frequency domain, which is more conducive to ECoG multi-classification tasks; 3) ECoG is acquired using 2D electrode array, the newly designed 2D Spatial-Temporal feature encoder can extract the 2D spatial feature better. Experiments have shown that the unique 2D spatial data structure can effectively improve classification accuracy; 3) Compared with previous work, the Bi-Band ECoGNet model is smaller and has higher performance, with an accuracy increase of 1.24%, and the model training speed is increased by 6 times, which is more suitable for BCI applications.
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